{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 线性回归预测房价"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**任务:**\n",
    "基于usa_housing_price.csv数据，建立线性回归模型，预测合理房价：\n",
    "1、以面积为输入变量，建立单因子模型，评估模型表现，可视化线性回归预测结果\n",
    "2、以income、house age、numbers of rooms、population、area为输入变量，建立多因子模型，评估模型表现\n",
    "3、预测Income=65000, House Age=5, Number of Rooms=5, Population=30000,size=200的合理房价\n",
    "X_test = [65000,5,5,30000,200]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "@Author  : Flare Zhao\n",
    "@QQ-Email & Wechat: 454209979\n",
    "@QQ讨论群：530533630  申请加群的验证信息为订单号（粘贴号码数字即可）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Area Population</th>\n",
       "      <th>size</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.45857</td>\n",
       "      <td>5.317139</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>23086.80050</td>\n",
       "      <td>188.214212</td>\n",
       "      <td>1.059034e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.64245</td>\n",
       "      <td>4.997100</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>40173.07217</td>\n",
       "      <td>160.042526</td>\n",
       "      <td>1.505891e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.06718</td>\n",
       "      <td>5.134110</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>36882.15940</td>\n",
       "      <td>227.273545</td>\n",
       "      <td>1.058988e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.24005</td>\n",
       "      <td>3.811764</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>34310.24283</td>\n",
       "      <td>164.816630</td>\n",
       "      <td>1.260617e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.19723</td>\n",
       "      <td>5.959445</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>26354.10947</td>\n",
       "      <td>161.966659</td>\n",
       "      <td>6.309435e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "0       79545.45857             5.317139                   7.009188   \n",
       "1       79248.64245             4.997100                   6.730821   \n",
       "2       61287.06718             5.134110                   8.512727   \n",
       "3       63345.24005             3.811764                   5.586729   \n",
       "4       59982.19723             5.959445                   7.839388   \n",
       "\n",
       "   Area Population        size         Price  \n",
       "0      23086.80050  188.214212  1.059034e+06  \n",
       "1      40173.07217  160.042526  1.505891e+06  \n",
       "2      36882.15940  227.273545  1.058988e+06  \n",
       "3      34310.24283  164.816630  1.260617e+06  \n",
       "4      26354.10947  161.966659  6.309435e+05  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('usa_housing_price.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "fig1 =plt.subplot(231) # 2行3列\n",
    "plt.scatter(data.loc[:,'Avg. Area Income'],data.loc[:,'Price'])\n",
    "plt.title('Price VS Income')\n",
    "\n",
    "fig2 =plt.subplot(232)\n",
    "plt.scatter(data.loc[:,'Avg. Area House Age'],data.loc[:,'Price'])\n",
    "plt.title('Price VS House Age')\n",
    "\n",
    "fig3 =plt.subplot(233)\n",
    "plt.scatter(data.loc[:,'Avg. Area Number of Rooms'],data.loc[:,'Price'])\n",
    "plt.title('Price VS Number of Rooms')\n",
    "\n",
    "fig4 =plt.subplot(234)\n",
    "plt.scatter(data.loc[:,'Area Population'],data.loc[:,'Price'])\n",
    "plt.title('Price VS Area Population')\n",
    "\n",
    "fig5 =plt.subplot(235)\n",
    "plt.scatter(data.loc[:,'size'],data.loc[:,'Price'])\n",
    "plt.title('Price VS size')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000,)\n"
     ]
    }
   ],
   "source": [
    "#define X and y\n",
    "X = data.loc[:,'size']\n",
    "print(X.shape)\n",
    "y = data.loc[:,'Price']\n",
    "# y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 1)\n"
     ]
    }
   ],
   "source": [
    "X = np.array(X).reshape(-1,1) # 变成m行1列\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy_X&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set up the linear regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "LR1 = LinearRegression()\n",
    "#train the model\n",
    "LR1.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1276881.85636623 1173363.58767144 1420407.32457443 ... 1097848.86467426\n",
      " 1264502.88144558 1131278.58816273]\n"
     ]
    }
   ],
   "source": [
    "#calculate the price vs size\n",
    "y_predict_1 = LR1.predict(X)\n",
    "print(y_predict_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108771672553.62639 0.1275031240418235\n"
     ]
    }
   ],
   "source": [
    "#evaluate the model\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "mean_squared_error_1 = mean_squared_error(y,y_predict_1)\n",
    "r2_score_1 = r2_score(y,y_predict_1)\n",
    "print(mean_squared_error_1,r2_score_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6 = plt.figure(figsize=(8,5))\n",
    "plt.scatter(X,y)  # 原始数据的散点图\n",
    "plt.plot(X,y_predict_1,'r') # 预测的结果\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Area Population</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.45857</td>\n",
       "      <td>5.317139</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>23086.80050</td>\n",
       "      <td>188.214212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.64245</td>\n",
       "      <td>4.997100</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>40173.07217</td>\n",
       "      <td>160.042526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.06718</td>\n",
       "      <td>5.134110</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>36882.15940</td>\n",
       "      <td>227.273545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.24005</td>\n",
       "      <td>3.811764</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>34310.24283</td>\n",
       "      <td>164.816630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.19723</td>\n",
       "      <td>5.959445</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>26354.10947</td>\n",
       "      <td>161.966659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>60567.94414</td>\n",
       "      <td>3.169638</td>\n",
       "      <td>6.137356</td>\n",
       "      <td>22837.36103</td>\n",
       "      <td>161.641403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>78491.27543</td>\n",
       "      <td>4.000865</td>\n",
       "      <td>6.576763</td>\n",
       "      <td>25616.11549</td>\n",
       "      <td>159.164596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>63390.68689</td>\n",
       "      <td>3.749409</td>\n",
       "      <td>4.805081</td>\n",
       "      <td>33266.14549</td>\n",
       "      <td>139.491785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>68001.33124</td>\n",
       "      <td>5.465612</td>\n",
       "      <td>7.130144</td>\n",
       "      <td>42625.62016</td>\n",
       "      <td>184.845371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>65510.58180</td>\n",
       "      <td>5.007695</td>\n",
       "      <td>6.792336</td>\n",
       "      <td>46501.28380</td>\n",
       "      <td>148.589423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "0          79545.45857             5.317139                   7.009188   \n",
       "1          79248.64245             4.997100                   6.730821   \n",
       "2          61287.06718             5.134110                   8.512727   \n",
       "3          63345.24005             3.811764                   5.586729   \n",
       "4          59982.19723             5.959445                   7.839388   \n",
       "...                ...                  ...                        ...   \n",
       "4995       60567.94414             3.169638                   6.137356   \n",
       "4996       78491.27543             4.000865                   6.576763   \n",
       "4997       63390.68689             3.749409                   4.805081   \n",
       "4998       68001.33124             5.465612                   7.130144   \n",
       "4999       65510.58180             5.007695                   6.792336   \n",
       "\n",
       "      Area Population        size  \n",
       "0         23086.80050  188.214212  \n",
       "1         40173.07217  160.042526  \n",
       "2         36882.15940  227.273545  \n",
       "3         34310.24283  164.816630  \n",
       "4         26354.10947  161.966659  \n",
       "...               ...         ...  \n",
       "4995      22837.36103  161.641403  \n",
       "4996      25616.11549  159.164596  \n",
       "4997      33266.14549  139.491785  \n",
       "4998      42625.62016  184.845371  \n",
       "4999      46501.28380  148.589423  \n",
       "\n",
       "[5000 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define X_multi 多因子\n",
    "X_multi = data.drop(['Price'],axis=1) # 去掉Price列\n",
    "X_multi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy_X&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set up 2nd linear model\n",
    "LR_multi = LinearRegression()\n",
    "#train the model\n",
    "LR_multi.fit(X_multi,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1223968.89166087 1497306.3318863  1250884.31019438 ... 1020693.92390375\n",
      " 1260503.36914585 1302737.7915763 ]\n"
     ]
    }
   ],
   "source": [
    "#make prediction\n",
    "y_predict_multi = LR_multi.predict(X_multi)\n",
    "print(y_predict_multi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10219846512.177862 0.9180229195220739\n"
     ]
    }
   ],
   "source": [
    "mean_squared_error_multi = mean_squared_error(y,y_predict_multi)\n",
    "r2_score_multi = r2_score(y,y_predict_multi)\n",
    "print(mean_squared_error_multi,r2_score_multi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108771672553.6264\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#多因子预测图\n",
    "fig7 = plt.figure(figsize=(8,5))\n",
    "plt.scatter(y,y_predict_multi)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单因子预测图\n",
    "fig8 = plt.figure(figsize=(8,5))\n",
    "plt.scatter(y,y_predict_1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[65000     5     5 30000   200]]\n"
     ]
    }
   ],
   "source": [
    "# 对测试数据进行预测\n",
    "X_test = [65000,5,5,30000,200]\n",
    "X_test = np.array(X_test).reshape(1,-1)\n",
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[817052.19516298]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dev\\anaconda3\\envs\\sklearn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y_test_predict = LR_multi.predict(X_test)\n",
    "print(y_test_predict)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "线性回归房价实战summary：\n",
    "1、通过搭建线性回归模型，实现单因子的房屋价格预测；\n",
    "2、在单因子模型效果不好的情况下，通过考虑更多的因子，建立了多因子模型；\n",
    "3、多因子模型达到了更好的预测效果，r2分数为0.91；\n",
    "4、实现了预测结果的可视化，直观对比预测价格与实际价格的差异。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:sklearn]",
   "language": "python",
   "name": "conda-env-sklearn-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
